where \(\eta _{ui}\)  describes the probability of positive preference (i.e., \(r_{ui}=1\) ) and is approximated by \(f_\theta (u,i)\) . \(\theta \)  denotes the parameters of \(f\) . We use \(f_\theta \)  as our target recommender that generates final recommendation. Generally speaking, \(f_\theta \)  is expected to have high model expressiveness. Since the denoising signal comes from the comparison between different model predictions, we introduce another auxiliary Bernoulli distribution parameterized by \(g_\mu (u,i)\) , In this paper, we use a relatively simple model matrix factorization (MF) [1]} as the auxiliary model \(g_\mu \) . Recent research has demonstrated that MF is still one of the most effective model to capture user preference for recommendation [2]}.
